Catálogo de publicaciones - libros

Self-calibration methods allow estimating the intrinsic camera parameters without using a known calibration object. However, such methods are very sensitive to noise, even in the simple special case of a purely rotating camera. Suitable pan-tilt-units can be used to perform pure camera rotations. In this case, we can get partial knowledge of the rotations, e.g. by rotating twice about the same axis. We present extended self-calibration algorithms which use such knowledge. In systematic simulations, we show that our new algorithms are less sensitive to noise. Experiments on real data result in a systematic error caused by non-ideal hardware. However, our algorithms can reduce the systematic error. In the case of complete rotation knowledge, it can even be greatly reduced.

Real-time active 3D range cameras based on time-of-flight technology using the Photonic Mixer Device (PMD) can be considered as a complementary technique for stereo-vision based depth estimation. Since those systems directly yield 3D measurements, they can also be used for initializing vision based approaches, especially in highly dynamic environments. Fusion of PMD depth images with passive intensity-based stereo is a promising approach for obtaining reliable surface reconstructions even in weakly textured surface regions.

In this work a PMD-stereo fusion algorithm for the estimation of patchlets from a combined PMD-stereo camera rig will be presented. As patchlet we define an oriented small planar 3d patch with associated surface normal. Least-squares estimation schemes for estimating patchlets from PMD range images as well as from a pair of stereo images are derived. It is shown, how those two approaches can be fused into one single estimation, that yields results even if either of the two single approaches fails.

Robot self-localization using a hemispherical camera system can be done without correspondences. We present a view-based approach using view descriptors, which enables us to efficiently compare the image signal taken at different locations. A compact representation of the image signal can be computed using Spherical Harmonics as orthonormal basis functions defined on the sphere. This is particularly useful because rotations between two representations can be found easily. Compact view descriptors stored in a database enable us to compute a likelihood for the current view corresponding to a particular position and orientation in the map.

We present an approach for estimating the 3D position and in case of articulated objects also the joint configuration from segmented 2D images. The pose estimation without initial information is a challenging optimization problem in a high dimensional space and is essential for texture acquisition and initialization of model-based tracking algorithms. Our method is able to recognize the correct object in the case of multiple objects and estimates its pose with a high accuracy. The key component is a particle-based global optimization method that converges to the global minimum similar to simulated annealing. After detecting potential bounded subsets of the search space, the particles are divided into clusters and migrate to the most attractive cluster as the time increases. The performance of our approach is verified by means of real scenes and a quantative error analysis for image distortions. Our experiments include rigid bodies and full human bodies.

The projection of fixed patterns in active 3d measurement systems is deteriorated by ambient lighting. Moreover classic projection patterns lead to ambiguities during pattern detection in the digital signal processing phase. Therefore a dynamic, diffraction based pattern projection system is introduced which can adapt to ambient lighting conditions. For error-free laser pattern detection a method for the design of unambiguous projection patterns is presented.

In the two-view case, point matching constraints are represented by the fundamental matrix. In the three-view case, the point matching constraints are indirectly represented by three trifocal tensors corresponding to the three camera matrices. A direct representation of the point matching constraints can be obtained by applying suitable transformations on the trifocal tensors. This paper discusses some issues related to point matching constraints. First, it presents a novel approach for deriving the constraints in terms of a generator space. Second, it shows that the resulting set of linearly independent constraints is 10-dimensional for the three-view case, a result which deviates from the literature on this subject. Third, in the case that the cameras have non-co-linear focal points, 9 of these 10 constraints can be obtained in a straight-forward way from the three fundamental matrices which we have in the three-view case. The last constraint can be obtained from the fundamental matrices but in a non-trivial way. The main result of the paper is a better understanding of the properties related to point matching constraints in three dimensions and how they are related to the corresponding two-view constraints.

This paper presents a novel multi-view camera system that produces real-time single view scene video which sees through the static objects to observe the dynamic objects. The system employs a training phase to recover the correspondences and occlusions between the views to determine the image positions where seeing through would be necessary. During the runtime phase, each dynamic object is detected and automatically registered between the views. The registered objects are learned using an appearance based method and they are later used to superimpose the occluded dynamic objects on the desired view. The occlusion detection is done using a very efficient and effective method. The system is very practical and can be used in real life applications including video surveillance, communication, activity analysis, and entertainment. We validated the system by running various tests in office and outdoor environments.

This contribution presents a novel approach to the challenging problem of model selection in motion estimation from sequences of images. New light is cast on parametric models of local optical flow. These models give rise to parameter estimation problems with highly correlated errors in variables (EIV). Regression is hence performed by equilibrated total least squares. The authors suggest to adaptively select motion models by testing local empirical regression residuals to be in accordance with the probability distribution that is theoretically predicted by the EIV model. Motion estimation with residual-based model selection is examined on artificial sequences designed to test specifically for the properties of the model selection process. These simulations indicate a good performance in the exclusion of inappropriate models and yield promising results in model complexity control.

A simple, stable and generic approach for estimation of relative positions and orientations of multiple rigidly coupled cameras is presented in this paper. The algorithm does not impose constraints on the field of view of the cameras and works even in the extreme case when the sequences from the different cameras are totally disjoint (i.e. when no part of the scene is captured by more than one camera). The influence of the rig motion on the existence of a unique solution is investigated and degenerate rig motions are identified. Each camera captures an individual sequence which is afterwards processed by a structure and motion (SAM) algorithm resulting in positions and orientations for each camera. The unknown relative transformations between the rigidly coupled cameras are estimated utilizing the rigidity constraint of the rig.

The measurement of fluid flows is an emerging field for optical flow computation. In a number of such applications, a tracer is visualized with modern digital cameras. Due to the projective nature of the imaging process, the tracer is integrated across a velocity profile. In this contribution, a novel technique is presented that explicitly models brightness changes due to this integration. Only through this modeling is an accurate estimation of the flow velocities feasible. Apart from an accurate measurement of the fluid flow, also the underlying velocity profile can be reconstructed. Applications from shear flow, microfluidics and a biological applications are presented.